arXiv subjects

Lin Chen

Publications and source records attributed to Lin Chen.

At least 397 records · Page 22Linked to original sources

arXiv2020

Constructing three-qubit unitary gates in terms of Schmidt rank and CNOT gates

It is known that every two-qubit unitary operation has Schmidt rank one, two or four, and the construction of three-qubit unitary gates in terms of Schmidt rank remains an open problem. We explicitly construct the gates of Schmidt rank from one to seven. It turns out that the three-qubit Toffoli and Fredkin gate respectively have Schmidt rank two and four. As an application, we implement the gates using quantum circuits of CNOT gates and local Hadamard and flip gates. In particular, the collective use of three CNOT gates can generate a three-qubit unitary gate of Schmidt rank seven in terms of the known Strassen tensor from multiplicative complexity. Our results imply the connection between the number of CNOT gates for implementing multiqubit gates and their Schmidt rank.

quant-ph↗

arXiv2020

Prioritizing documentation effort: Can we do better?

Code documentations are essential for software quality assurance, but due to time or economic pressures, code developers are often unable to write documents for all modules in a project. Recently, a supervised artificial neural network (ANN) approach is proposed to prioritize important modules for documentation effort. However, as a supervised approach, there is a need to use labeled training data to train the prediction model, which may not be easy to obtain in practice. Furthermore, it is unclear whether the ANN approach is generalizable, as it is only evaluated on several small data sets. In this paper, we propose an unsupervised approach based on PageRank to prioritize documentation effort. This approach identifies "important" modules only based on the dependence relationships between modules in a project. As a result, the PageRank approach does not need any training data to build the prediction model. In order to evaluate the effectiveness of the PageRank approach, we use six additional large data sets to conduct the experiments in addition to the same data sets collected from open-source projects as used in prior studies. The experimental results show that the PageRank approach is superior to the state-of-the-art ANN approach in prioritizing important modules for documentation effort. In particular, due to the simplicity and effectiveness, we advocate that the PageRank approach should be used as an easy-to-implement baseline in future research on documentation effort prioritization, and any new approach should be compared with it to demonstrate its effectiveness.

cs.SE↗

arXiv2020

Tripartite genuinely entangled states from entanglement-breaking subspaces

The determination of genuine entanglement is a central problem in quantum information processing. We investigate the tripartite state as the tensor product of two bipartite entangled states by merging two systems. We show that the tripartite state is a genuinely entangled state when the range of both bipartite states are entanglement-breaking subspaces. We further investigate the tripartite state when one of the two bipartite states has rank two. Our results provide the latest progress on a conjecture proposed in the paper [Yi Shen $\textit{et al}$, J. Phys. A 53, 125302 (2020)]. We apply our results to construct multipartite states whose bipartite reduced density operators have additive EOF. Further, such states are distillable across every bipartition under local operations and classical communications.

quant-ph↗

arXiv2020

Constructions of $k$-uniform states from mixed orthogonal arrays

We study $k$-uniform states in heterogeneous systems whose local dimensions are mixed. Based on the connections between mixed orthogonal arrays with certain minimum Hamming distance, irredundant mixed orthogonal arrays and $k$-uniform states, we present two constructions of $2$-uniform states in heterogeneous systems. We also construct a family of $3$-uniform states in heterogeneous systems, which solves a question posed in [D. Goyeneche et al., Phys. Rev. A 94, 012346 (2016)]. We also show two methods of generating $(k-1)$-uniform states from $k$-uniform states. Some new results on the existence and nonexistence of absolutely maximally entangled states are provided. For the applications, we present an orthogonal basis consisting of $k$-uniform states with minimum support. Moreover, we show that some $k$-uniform bases can not be distinguished by local operations and classical communications, and this shows quantum nonlocality with entanglement.

quant-ph↗

arXiv2020

The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data. In this paper, however, we challenge this conventional belief and show that more training data can hurt the generalization of adversarially robust models in the classification problems. We first investigate the Gaussian mixture classification with a linear loss and identify three regimes based on the strength of the adversary. In the weak adversary regime, more data improves the generalization of adversarially robust models. In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve, which implies the existence of an intermediate stage where more training data hurts the generalization. In the strong adversary regime, more data almost immediately causes the generalization error to increase. Then we move to the analysis of a two-dimensional classification problem with a 0-1 loss. We prove that more data always hurts the generalization performance of adversarially trained models with large perturbations. To complement our theoretical results, we conduct empirical studies on Gaussian mixture classification, support vector machines (SVMs), and linear regression.

cs.LG↗

arXiv2020

Detection of genuine tripartite entanglement by two bipartite entangled states

It is an interesting problem to construct genuine tripartite entangled states based on the collective use of two bipartite entangled states. We consider the case that the states are two-qubit Werner states, we construct the interval of parameter of Werner states such that the tripartite state is genuine entangled. Further, we present the way of detecting the tripartite genuine entanglement using current techniques in experiments. We also investigate the lower bound of genuine multipartite entanglement concurrence.

quant-ph↗

arXiv2020

Unextendible product bases from tile structures and their local entanglement-assisted distinguishability

We completely characterize the condition when a tile structure provides an unextendible product basis (UPB), and construct UPBs of different large sizes in $\mathbb{C}^m\otimes\mathbb{C}^n$ for any $n\geq m\geq 3$. This solves an open problem in [S. Halder et al., Phys. Rev. A 99, 062329 (2019)]. As an application, we show that our UPBs of size $(mn-4\lfloor\frac{m-1}{2}\rfloor)$ in $\mathbb{C}^m\otimes\mathbb{C}^n$ can be perfectly distinguished by local operations and classical communications assisted with a $\lceil\frac{m}{2}\rceil\otimes\lceil\frac{m}{2}\rceil$ maximally entangled state.

quant-ph↗

arXiv2020

Hybrid Cryptocurrency Pump and Dump Detection

Increasingly growing Cryptocurrency markets have become a hive for scammers to run pump and dump schemes which is considered as an anomalous activity in exchange markets. Anomaly detection in time series is challenging since existing methods are not sufficient to detect the anomalies in all contexts. In this paper, we propose a novel hybrid pump and dump detection method based on distance and density metrics. First, we propose a novel automatic thresh-old setting method for distance-based anomaly detection. Second, we propose a novel metric called density score for density-based anomaly detection. Finally, we exploit the combination of density and distance metrics successfully as a hybrid approach. Our experiments show that, the proposed hybrid approach is reliable to detect the majority of alleged P & D activities in top ranked exchange pairs by outperforming both density-based and distance-based methods.

cs.AI↗

arXiv2020

TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning

The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obtain. The few-shot learning problem has attracted increasing attention from researchers for building a robust model upon only a few labeled samples. Most existing works tackle this problem under the meta-learning framework by mimicking the few-shot learning task with an episodic training strategy. In this paper, we propose a new transfer-learning framework for semi-supervised few-shot learning to fully utilize the auxiliary information from labeled base-class data and unlabeled novel-class data. The framework consists of three components: 1) pre-training a feature extractor on base-class data; 2) using the feature extractor to initialize the classifier weights for the novel classes; and 3) further updating the model with a semi-supervised learning method. Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with Imprinting and MixMatch. Extensive experiments on two popular benchmark datasets for few-shot learning, CUB-200-2011 and miniImageNet, demonstrate that our proposed method can effectively utilize the auxiliary information from labeled base-class data and unlabeled novel-class data to significantly improve the accuracy of few-shot learning task.

cs.LG↗

arXiv2020

Terahertz composite plasmonic slabs based on double-layer metallic gratings

A composite plasmonic slab based on double-layer metallic gratings and a dielectric film is experimentally and numerically demonstrated in terahertz (THz) regions, which can support two resonance modes and then form a broad bandgap (40%). As compared to the double-layer metal grating, the dielectric film in composite THz slabs significantly enhances the transmission of the transverse magnetic (TM) mode. Electric field vector proved that the low-frequency resonance mode originates from the symmetric plasmonic mode and the high-frequency resonance mode is induced by the hybrid mode of plasmonic and dielectric modes. The inherently near field coupling between metal gratings and dielectric film has been analyzed by changing the structural parameters. We further demonstrate that by tuning the metallic grating width, the plasmonic bandgap can be manipulated. These results suggest that this composite plasmonic slab has great potential for use as a filter, polarizer, and sensor in THz regions.

physics.optics↗

arXiv2020

Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided. For a monotone and continuous DR-submodular function, and subject to a bounded convex body constraint, we propose Black-box Continuous Greedy, a derivative-free algorithm that provably achieves the tight $[(1-1/e)OPT-ε]$ approximation guarantee with $O(d/ε^3)$ function evaluations. We then extend our result to the stochastic setting where function values are subject to stochastic zero-mean noise. It is through this stochastic generalization that we revisit the discrete submodular maximization problem and use the multi-linear extension as a bridge between discrete and continuous settings. Finally, we extensively evaluate the performance of our algorithm on continuous and discrete submodular objective functions using both synthetic and real data.

cs.LG↗

arXiv2020

Terahertz metallic photonic crystals integrated with dielectric waveguides

Compact and low-loss photonic crystal waveguides are critical in integrated terahertz (THz) applications. Compared with pure metal or dielectric photonic crystal waveguides, hybrid (metal-dielectric) integrated waveguides provide a simple way to further improve the field confinement and the propagation loss. In this work, we investigate a hybrid waveguide consisting of metallic photonic crystals and dielectric films in 0.1-1.0 THz. Photonic crystal waveguides based on metal pillar arrays (MPAs) support two resonance modes including the fundamental and high-order transverse magnetic (TM) modes and then form one apparent bandgap in 0.45-0.55 THz. The high-order TM-mode shows higher confinement than the fundamental mode and are thus sensitive to the dielectric film on the MPAs. The propagation loss and field confinement can be optimized by changing the dielectric film thickness and refractive index. The investigation shows that the lowest loss occurs at 0.68 THz because the high-order TM-mode THz waves are tightly confined inside the hybrid waveguide. This work proves that such hybrid waveguides based on metallic photonic crystals are promising to develop as a compact integrated terahertz device.

physics.optics↗

arXiv2020

Deep Fusion of Local and Non-Local Features for Precision Landslide Recognition

Precision mapping of landslide inventory is crucial for hazard mitigation. Most landslides generally co-exist with other confusing geological features, and the presence of such areas can only be inferred unambiguously at a large scale. In addition, local information is also important for the preservation of object boundaries. Aiming to solve this problem, this paper proposes an effective approach to fuse both local and non-local features to surmount the contextual problem. Built upon the U-Net architecture that is widely adopted in the remote sensing community, we utilize two additional modules. The first one uses dilated convolution and the corresponding atrous spatial pyramid pooling, which enlarged the receptive field without sacrificing spatial resolution or increasing memory usage. The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map. In implementation, the computational overhead against the original U-Net was only a few convolutional layers. Experimental evaluations revealed that the proposed method outperformed state-of-the-art general-purpose semantic segmentation approaches. Furthermore, ablation studies have shown that the two models afforded extensive enhancements in landslide-recognition performance.

eess.IV↗

arXiv2020

Absolutely maximally entangled states in tripartite heterogeneous systems

Absolutely maximally entangled (AME) states are typically defined in homogeneous systems. However, the quantum system is more likely to be heterogeneous in a practical setup. In this work we pay attention to the construction of AME states in tripartite heterogeneous systems. We first introduce the concept of irreducible AME states as the basic elements to construct AME states with high local dimensions. Then we investigate the tripartite heterogeneous systems whose local dimensions are $l,m,n$, with $3\leq l<m<n\leq m+l-1$. We show the existence of AME states in such heterogeneous systems is related to a kind of arrays called magic solution array. We further identify the AME states in which kinds of heterogeneous systems are irreducible. In addition, we propose a method to construct $k$-uniform states of more parties using two existing AME states. We also build the connection between heterogeneous AME states and multi-isometry matrices, and indicate an application in quantum steering.

quant-ph↗

arXiv2020

Probing Jet Medium Interactions via $Z$($H$)+jet Momentum Imbalances

Different types of high energy hard probes are used to extract the jet transport properties of the Quark-Gluon Plasma created in heavy-ion collisions, of which the heavy boson tagged jets are undoubtedly the most sophisticated due to its clean decay signature and production mechanism. In this study, we used the resummation improved pQCD approach with high order correction in the hard factor to calculate the momentum ratio $x_J$ distributions of $Z$ and Higgs($H$) tagged jets. We found that the formalism can provide a good description of the 5.02 TeV $pp$ data. Using the BDMPS energy loss formalism, along with the OSU 2+1D hydro to simulate the effect of the medium, we extracted the value of the jet transport coefficient to be around $\hat{q}_0=4\sim8~GeV^2/fm$ by comparing with the $Z$+jet $PbPb$ experimental data. The $H$+jet $x_J$ distribution were calculated in a similar manner in contrast and found to have a stronger Sudakov effect as compared with the $Z$+jet distribution. This study uses a clean color-neutral boson as trigger to study the jet quenching effect and serves as a complimentary method in the extraction of the QGP's transport coefficient in high energy nuclear collisions.

hep-ph↗

arXiv2020

Differentially Private Combinatorial Cloud Auction

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.

cs.CR↗

arXiv2019

An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning

With the advent of the era of big data, deep learning has become a prevalent building block in a variety of machine learning or data mining tasks, such as signal processing, network modeling and traffic analysis, to name a few. The massive user data crowdsourced plays a crucial role in the success of deep learning models. However, it has been shown that user data may be inferred from trained neural models and thereby exposed to potential adversaries, which raises information security and privacy concerns. To address this issue, recent studies leverage the technique of differential privacy to design private-preserving deep learning algorithms. Albeit successful at privacy protection, differential privacy degrades the performance of neural models. In this paper, we develop ADADP, an adaptive and fast convergent learning algorithm with a provable privacy guarantee. ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise. The performance of ADADP is evaluated on real-world datasets. Experiment results show that it outperforms state-of-the-art differentially private approaches in terms of both privacy cost and model accuracy.

cs.CR↗

arXiv2019

A Dual-gate MoS2 Photodetector Based on Interface Coupling Effect

Two-dimensional (2D) transition metal dichalcogenides (TMDs) based photodetectors have shown great potential for the next generation optoelectronics. However, most of the reported MoS2 photodetectors function under the photogating effect originated from the charge-trap mechanism, which is difficult for quantitative control. Such devices generally suffer from a poor compromise between response speed and responsivity (R) and large dark current. Here, a dual-gated (DG) MoS2 phototransistor operating based on the interface coupling effect (ICE) is demonstrated. By simultaneously applying a negative top-gate voltage (VTG) and positive back-gate voltage (VBG) to the MoS2 channel, the photo-generated holes can be effectively trapped in the depleted region under TG. An ultrahigh R of ~1E5 A/W and detectivity (D*) of ~1E14 Jones have been achieved in several devices with different thickness under Pin of 53 uW/cm2 at VTG=-5 V. Moreover, the response time of the DG phototransistor can also be modulated based on the ICE. Based on these systematic measurements of MoS2 DG phototransistors, the results show that the ICE plays an important role in the modulation of photoelectric performances. Our results also pave the way for the future optoelectrical application of 2D TMDs materials and prompt for further investigation in the DG structured phototransistors.

physics.app-ph↗